In this thesis novel configurations and operating strategies in the mixed suspension mixed product removal (MSMPR) crystalliser are investigated, aided by integrated process analytical technologies (PAT) and crystallisation informatics system (CryPRINS) tools. The MSMPR is an idealised crystalliser model that assumes: steady-state operation; well mixed suspension with no product classification, such that all volume elements contain a mixture of particles (small and large) and crystal size distribution (CSD) that is independent of location in the crystalliser and is identical of the product withdrawn; and uniform supersaturation thought, leading to constant nucleation and growth rates. Single-stage MSMPR designs with continuous recycle/recirculation and modified heat exchanger were investigated and found to minimise fouling, encrustation and transfer line blockages. In particular, a modified MSMPR with baffled heat exchanger was found to significantly reduce the temperature between incoming feed hot feed solution and the cooled crystalliser, leading to a significant reduction in fouling, encrustation and blockages. In addition, the concept of the periodic mixed suspension mixed product removal (PMSMPR) crystallisation process is demonstrated for the first time viz single- and multi-stage cascaded operations. This method of operation involves the periodic transfer of slurry (addition and withdrawal) at high flow rates from either a single stirred vessel or between a number of stirred vessels arranged in series. The PMSMPR is therefore characterised by periodic withdrawals of product slurry. Similar to the MSMPR, the product withdrawn from a PMSMPR has exactly the same composition as the vessel at the time of removal. The rapid withdrawal of slurry at high flow rates in PMSMPR operation leads to the prevention of particle sedimentation and blockage of transfer lines. The transfer of slurry (to/from) the PMSMPR is followed by a holding (or pause) period when no addition or withdrawal of slurry takes place. The holding period extends the mean residence time of the PMSMPR relative to a typical MSMPR, thereby increasing the yield and productivity of crystallisation as more time is allowed for consumption of available supersaturation viz crystal growth and nucleation. A state of controlled operation (SCO) in the periodic flow process, defined as a state of the system that maintains itself despite regular, but controlled disruptions was characterised using the PAT tools and CryPRINS within an intelligent decision support (IDS) framework. The crystallisation of paracetamol (PCM) from isopropyl alcohol (IPA) using different configurations of a single-stage continuous MSMPR crystalliser that incorporated continuous recycle and recirculation loop, and a novel design with baffled heat exchanger was investigated. Crystallisations of PCM-IPA carried out in the MSMPR without heat exchanger suffered from severe fouling, encrustation and blockage problems due to the high level of supersaturation (S = 1.39) in the crystalliser, which was required for the initial burst of nucleation to generate enough particles for later growth, as well as the large temperature difference between the incoming feed (45 oC) and the crystalliser (10 oC). Using the modified MSMPR design with baffled heat exchanger, the challenges of fouling, encrustation and blockage were significantly reduced due to the rapid lowering of the feed stream temperature prior to entering the crystalliser. In addition, the closed loop system led to conservation of material, which is a great benefit since large amounts of materials would otherwise be required if the MSMPR was operated with continuous product removal. This design is great for research purposes, in particular, to investigate process design and optimisation. Continuous crystallisation of PCM in the presence of hydroxyl propyl methyl cellulose (HPMC) additive was investigated in the modified MSMPR design with heat exchanger. HPMC was found to improve the crystallisation performance, leading to complete avoidance of fouling, encrustation and blockages at a concentration of 0.05 wt%. However, the yield of crystallisation was significantly reduced (28.0 %) compared to a control experiment (98.8 %, biased due to fouling/encrustation) performed without additive addition. Regardless, the productivity of crystallisation was more than four times that achieved in batch linear cooling (LC) (0.62 0.86 g/L-min) and batch automated dynamic nucleation control (ADNC) (0.24 0.25 g/L-min) runs. Aspects of the periodic flow crystallisation of single- and multi-component (co-crystals) molecular systems have also been examined to demonstrate the concept of state of controlled operation . The single component systems studied were PCM and glycine (GLY), each representative of compounds with slow and fast growth kinetics, respectively. The co-crystal systems investigated were urea-barbituric acid (UBA) and p Toluenesulfonamide-Triphenylphosphine oxide (p-TSA-TPPO). UBA is a polymorphic co-crystal system with three known forms (I, II and III). Form I UBA was successfully isolated in a three-stage periodic flow PMSMPR crystalliser. This study demonstrates the capability of periodic flow crystallisation for isolation of a desired polymorph from a mixture. p-TSA-TPPO exists in two known stoichiometric co-crystal forms, 1:1 and 3:2 mole ratio p-TSA-TPPO, respectively. The two crystalline forms exhibit solution mediated transformation, which proves to be a difficulty for separation. For this study, the implementation of temperature cycles in batch and flow control in semi-batch and periodic PMSMPR crystallisers were investigated to isolate pure 1:1 and 3:2 p-TSA-TPPO, respectively. Different regions of the ternary diagram of p-TSA, TPPO and acetonitrile (MeCN) were investigated. The desired co-crystal form was isolated all crystallisation platforms investigated. However, greater consistency was observed in the semi-batch and PMSMPR operations respectively. Periodic flow crystallisation in PMSMPR is a promising alternative to conventional continuous MSMPR operation, affording greater degrees of freedom operation, slightly narrower RTD profiles, consistent product crystal quality (size, shape and distribution), longer mean residence times, higher yield and productivity and significant reduction in fouling, encrustation and transfer line blockages over prolonged operating periods. Furthermore, the PMSMPR is a versatile platform that can be used to investigate a range of different molecular systems. Relative to batch operation, the PMSMPR can operate close to equilibrium, however, this is dependent on the system kinetics. In addition, retrofitting of batch crystallisers to operate as PMSMPRS fairly simple and require only subtle changes to the existing design space. The integrated array of PAT sensors consisted of attenuated total reflectance ultra violet/visible spectroscopy (ATR-UV/vis), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), focused beam reflectance measurement (FBRM), particle vision microscopy (PVM) and Raman spectroscopy. The results from the studies reported here illustrate very well the use of PAT and information system tools together to determine when the continuous and periodic MSMPR operations reaches a steady-state or state of controlled operation (i.e. periodic steady-state). These tools provided a better understanding of the variables and operating procedures that influence the two types of operations.

Monoklonala antikroppsbaserade läkemedel (mAb) är ett av de snabbast växande segmenten på läkemedelsmarknaden, främst på grund av deras tillämpning inom onkologi, immunologi och hematologi. Traditionellt sker den industriella produktionen av mAb med fed-batch-odling. Detta är en relativt lätthanterlig process med mAb-utbyten på 5-10 g/L, men dess brist på kontroll över kritiska processparametrar (CPP) orsakar höga mAb-förluster på grund av att kvalitetsspecifikationer inte uppfylls. Ökande marknadskrav och regulatoriska förändringar pådriver läkemedelsindustrin iinnovation inom mAb-tillverkningsprocessen, för att nå kontinuerlig tillverkning. För närvarande, som ett övergångssteg till kontinuerlig tillverkning, sker investeringar i intensifierade fed-batch-odlingar. Dessa uppnår högre celldensiteter på cirka 25-30 g/L, men detta är fortfarande mycket lägre än motsvarande mAb-koncentrationer på 130 g/L som kan uppnås med perfusionsprocesser. Andra fördelar med perfusionsprocesser är att de tillåter flexibla produktionsanläggningar och möjliggör en nivå av processkontroll som skulle tillåta realtidstestning av release. För att upprätthålla en perfusionsprocess under de specificerade förhållandena som garanterar den önskade mAb-kvaliteten, måste CPP kontrolleras noggrant. Process Analytical Technologies (PAT) kan mäta CPP i realtid på ett icke-destruktivt sätt. Denna studie undersökte tillämpningen av två PAT, ArgusEye®-sensorerna och Time-gated Raman-spektroskopi, på perfusionsprocesser. Vi visade att ArgusEye®-sensorerna kan användas för att mäta IgG i perfusionsprover med ganska bra korrelation med referensmetoden. Vi har också visat att multivariata Raman-baserade modeller kan konstrueras för att förutsäga flera CPP, baserat på samma spektra. Framförallt belyser denna studie komplexiteten i tillämpningen av dessa PAT för att kontrollera perfusionsprocesser. För ArgusEye® drar vi slutsatsen att för att få exakta mätningar måste vi ta hänsyn till förändringarna i koncentrationen av värdcellsprotein under en perfusionsprocess, eftersom deras ospecifika bindning till sensorerna är den troliga orsaken till variationen i IgG-mätningarna. För de Raman-baserade modellerna, visar denna studie att en stor mängd data krävs för att bygga korrekta prediktionsmodeller, något som rapprterats om i litteraturen. Sammantaget visar denna rapport att dessa PAT har en stor tillämpningspotential, men de måste förbättras ytterligare innan de kan användas som automatiska återkopplingskontrollverktyg. / Monoclonal antibody-based therapeutics (mAb) are one of the fastest-growing segments in the pharmaceutical market, mainly due to their application in oncology, immunology, and hematology. Traditionally, the industrial production of mAb is done with fed-batch cultivation. This is a relatively easy to operate process with mAb yields of 5-10 g/L, but its lack of control over critical process parameters (CPP) causes high mAb losses due to unmet quality specifications. Driven by increasing market demands and regulatory changes, the pharmaceutical industry is innovating in the mAb manufacturing process to reach continuous manufacturing. Currently, as a transition step to continuous manufacturing, the pharmaceutical industry is investing in intensified fed-batch cultivations. They achieve higher cells densities and present yields around 25-30 g/L, but this is still much lower than the equivalent mAb titers of 130 g/L that can be achieved with perfusion processes. Other advantages of perfusion processes are that they allow the existence of flexible production facilities and enable a level of process control that would permit Real-Time Release Testing. To maintain a perfusion process under the specified conditions to guarantee the desired mAb quality, the CPP need to be closely controlled. Process Analytical Technologies (PAT) can measure CPP in real-time and non-destructively. This study evaluated the application of two PAT, the ArgusEye® sensors and Time-gated Raman spectroscopy, on perfusion processes. We showed that the ArgusEye® sensors can be used to measure IgG in perfusion samples with quite good correlation to the reference method. We have also shown that multivariate Raman-based models can be constructed to predict several CPP based on the same spectra. Most importantly, this study highlights the complexity of the application of these PAT to control perfusion processes. For the ArgusEye®, we conclude that to obtain accurate measurements, we need to account for the changes in the concentration of host cell protein during a perfusion process, as their unspecific binding to the sensors is the probable cause for the variation in the IgG measurements. For the Raman-based models, as previously reported in the literature, this study shows that a high volume of data is require to build accurate prediction models. Overall, this report shows that these PAT have a great potential of application, but they need to be further improved prior to their use as automatic feedback control tools.

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>